glide-text2im
v-diffusion-pytorch
glide-text2im | v-diffusion-pytorch | |
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32 | 10 | |
3,470 | 690 | |
0.6% | - | |
0.0 | 0.0 | |
about 2 months ago | over 1 year ago | |
Python | Python | |
MIT License | MIT License |
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glide-text2im
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인공지능에 대한 이해 : https://youtu.be/g1ARrNTwBHg 1편 - 딥러닝의 원리 https://youtu.be/CA5Ggqg5x6o 2편 - 인공지능의 창의성과 테슬라 AI https://youtu.be/jHYYggG7qq8 3편 - 코딩, 과학, 수학 난제를 해결하려는 A.I. https://youtu.be/BWJWAdMZGNY ---------------------------------------------------- 영상에 등장하는 링크 : ADOP(2021) https://arxiv.org
GLIDE(2021) https://syncedreview.com/2021/12/24/deepmind-podracer-tpu-based-rl-frameworks-deliver-exceptional-performance-at-low-cost-173/ || 소스코드 : https://github.com/openai/glide-text2im
- [R][P] I made an app for Instant Image/Text to 3D using PointE from OpenAI
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"Teacher villainess, DreamWorks official character design sheet turnaround, studio, Best on Artstation, 4K HD, by Nate Wragg"
The bolded part is a reference to the publicly released version of OpenAI's GLIDE, which is the predecessor of DALL-E 2. OpenAI didn't release the GLIDE model(s) trained on human faces.
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Trying to remember the name of an upscaler. I thought it was Glide XL or something.
OpenAI's GLIDE text2im https://github.com/openai/glide-text2im
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It just struck me that text diffs do *not* require the image-generating prompt as a starting point, and my mind is blown to pieces.
If I can stop wasting my time playing video games for a while, I might work on getting the Dalle-2 open-source predecessor (GLIDE) to work. Also can't wait for this to be released, I have so many uses for it!
- [D] Making text-to-image even better - GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models, a 5-minute paper summary by Casual GAN Papers
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Dall-E 2
A few comments by someone who's spent way too much time in the AI-generated space:
* I recommend reading the System Card that came with it because it's very through: https://github.com/openai/dalle-2-preview/blob/main/system-c...
* Unlike GPT-3, my read of this announcement is that OpenAI does not intend to commercialize it, and that access to the waitlist is indeed more for testing its limits (and as noted, commercializing it would make it much more likely lead to interesting legal precedent). Per the docs, access is very explicitly limited: (https://github.com/openai/dalle-2-preview/blob/main/system-c... )
* A few months ago, OpenAI released GLIDE ( https://github.com/openai/glide-text2im ) which uses a similar approach to AI image generation, but suspiciously never received a fun blog post like this one. The reason for that in retrospect may be "because we made it obsolete."
* The images in the announcement are still cherry-picked, which is therefore a good reason why they tested DALL-E 1 vs. DALL-E 2 presumably on non-cherrypicked images.
* Cherry-picking is relevant because AI image generation is still slow unless you do real shenanigans that likely compromise image quality, although OpenAI has likely a better infra to handle large models as they have demonstrated with GPT-3.
- Glide-Text2Im
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AI-generated photos of European flags
The flags were generated using Glide. You can try it out yourself in Google Colab
- New AI technique that lets you generate images from text. Now better than ever!
v-diffusion-pytorch
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Leaked deck raises questions over Stability AI’s Series A pitch to investors
This is dumb.
We employed Eleuther team members as Stability AI employees/contractors and incubated them until the 501(c)3 was set up and we managed to bring in other funders too: https://techcrunch.com/2023/03/02/stability-ai-hugging-face-...
I am on the board and delighted to continue to support their work as an independent organisation for LM evaluation, alignment and interpretability which is much needed.
Indeed though our approach was handing out significant compute for no control, no equity, no IP.
Anyone who has received Stability AI grants will be able to attest to this with multiple breakthroughs as a result, for example funding https://github.com/BlinkDL/RWKV-LM, the work of https://github.com/lucidrains and others.
Similarly we funded the beta of MidJourney with a cash grant for compute without ever even floating asking for equity etc as it is a market-creating innovation.
At the time MidJourney was using cc12m_1, a model developed by one of our lead (employed) generative AI developers Katherine Crownson / RiversHaveWings (https://github.com/crowsonkb/v-diffusion-pytorch)
Our model is simply to take open innovation and create commercial variants of that (our stable series models) from scratch and on our own, plus variants of that for private data - https://twitter.com/EMostaque/status/1649152422634221593?s=2...
This means we can be hands off versus other funders and trust researchers and help them succeed, something others do not.
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[D] Is Midjourney AI more-or-less the same architecture as DALL-E 2? Can I read about the model in detail somewhere or is there anything published in this regard?
From what I've gathered by being involved early in the beta / in other discords, Midjourney was originally based on a fine-tuned version of classifier-free guided v-diffusion. The fine-tuning dataset was a manually curated set largely from LAION-2B similar to the laion-art / laion-hd. To make it so fast they were using Progressive Distillation (possibly distilling on PLMS steps rather than p/ddim?) and settings optimized to let them skip a few of the first steps like Quick CLIP-Guided Diffusion. There's a good chance they were doing some prompt augmentation as well, although I think this would be susceptible to prompt discovery attacks which I haven't seen any examples of for Midjourney.
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Tweet: "Give us a few weeks, open version in the works." regarding an open Google Imagen-like system
Source. This tweet is from a person whose organization has been publicly credited with providing compute for others in the past (example: "Thank you to stability.ai for compute to train these models!").
- Does anyone know which GAN this is?
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Dall-E 2
com/RiversHaveWings/status/1462859669454536711, 2021.
[8] Katherine Crowson. v-diffusion. https://github.com/crowsonkb/v-diffusion-pytorch, 2021.
- How do I start creating my own AI generated art?
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Advice on improving Text to Image Model (CC12M Diffusion) model at higher output dimensions?
More parameters are available as seen in this code. The fix was adapted from this.
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Colab notebook "Text to Image (CC12M Diffusion)" from RiversHaveWings was updated with significantly faster image generation speed. It generates 4 images in 4.75 minutes (not including setup time) on a Tesla K80 GPU (free-tier Colab).
I'm not sure if this Colab notebook was mentioned in this sub previously, but it's been available since January 2022. The cc12m_1_cfg model used by this Colab notebook is different than the cc12m_1 model from this December 2021 post (reference).
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Airport carpets (a genurary submission)
A few more here https://twitter.com/metasemantic/status/1486334535436488705. Samples careful constructed with a heavily modified diffusion model by @rivershavewings https://github.com/crowsonkb/v-diffusion-pytorch
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Steampunk Airships
Most of the code was from Katherine Crowson's (@RiversHaveWings) v-diffusion-pytorch library (https://github.com/crowsonkb/v-diffusion-pytorch), which is an implementation of denoising diffusion probabilistic models (https://arxiv.org/abs/2006.11239). I used the CC12M_1 CFG checkpoint.
What are some alternatives?
dalle-2-preview
dalle-mini - DALL·E Mini - Generate images from a text prompt
tensorrtx - Implementation of popular deep learning networks with TensorRT network definition API
glide-text2im-colab - Colab notebook for openai/glide-text2im.
gpt-3 - GPT-3: Language Models are Few-Shot Learners
pixray
improved-diffusion - Release for Improved Denoising Diffusion Probabilistic Models
jaxtorch - A JAX nn library
diffusion - Denoising Diffusion Probabilistic Models
jukebox - Code for the paper "Jukebox: A Generative Model for Music"